
@article{bakshy_exposure_2015,
	title = {Exposure to ideologically diverse news and opinion on {Facebook}},
	volume = {348},
	url = {https://www.science.org/doi/10.1126/science.aaa1160},
	doi = {10.1126/science.aaa1160},
	number = {6239},
	urldate = {2021-12-07},
	journal = {Science},
	author = {Bakshy, Eytan and Messing, Solomon and Adamic, Lada A.},
	month = jun,
	year = {2015},
	note = {Publisher: American Association for the Advancement of Science},
	pages = {1130--1132},
}

@article{prior_improving_2009,
	title = {Improving {Media} {Effects} {Research} through {Better} {Measurement} of {News} {Exposure}},
	volume = {71},
	issn = {0022-3816},
	url = {https://www.journals.uchicago.edu/doi/abs/10.1017/S0022381609090781},
	doi = {10.1017/S0022381609090781},
	abstract = {Survey research is necessary to understand media effects, but seriously impeded by considerable overreporting of news exposure, the extent of which differs across respondents. Consequently, apparent media effects may arise not because of differences in exposure, but because of differences in the accuracy of reporting exposure. Drawing on experiments embedded in two representative surveys, this study examines why many people overstate their exposure to television news. Analysis indicates that overreporting results from unrealistic demands on respondents’ memory, not their motivation to misrepresent or provide superficial answers. Satisficing and social desirability bias do not explain overreporting. Instead, imperfect recall coupled with the use of flawed inference rules causes inflated self-reports. To lower reports of news exposure and improve the validity of conclusions about media effects, researchers should help respondents with the estimation by providing population frequencies and encouraging comparison with others.},
	number = {3},
	urldate = {2021-12-07},
	journal = {The Journal of Politics},
	author = {Prior, Markus},
	month = jul,
	year = {2009},
	note = {Publisher: The University of Chicago Press},
	pages = {893--908},
}

@article{abramowitz_exploring_2006,
	title = {Exploring the {Bases} of {Partisanship} in the {American} {Electorate}: {Social} {Identity} vs. {Ideology}},
	volume = {59},
	issn = {1065-9129},
	shorttitle = {Exploring the {Bases} of {Partisanship} in the {American} {Electorate}},
	doi = {10.1177/106591290605900201},
	abstract = {This article uses data from the 1952-2004 American National Election Studies and the 2004 U.S. National Exit Poll to compare the influence of ideology and membership in social groups on party identification. Contrary to the claim by Green, Palmquist, and Schickler (2002) that party loyalties are rooted in voters' social identities, we find that party identification is much more strongly related to voters' ideological preferences than to their social identities as defined by their group memberships. Since the 1970s, Republican identification has increased substantially among whites inside and outside of the South with the most dramatic gains occurring among married voters, men, and Catholics. Within these subgroups, however, Republican gains have occurred mainly or exclusively among self-identified conservatives. As a result, the relationship between ideology and party identification has increased dramatically. This has important implications for voting behavior. Increased consistency between ideology and party identification has contributed to higher levels of party loyalty in presidential and congressional elections.},
	language = {eng},
	number = {2},
	journal = {Political research quarterly},
	author = {Abramowitz, Alan I. and Saunders, Kyle L.},
	year = {2006},
	note = {Place: Thousand Oaks, CA
Publisher: University of Utah},
	pages = {175--187},
}

@book{green_partisan_2002,
	address = {New Haven},
	series = {Yale {ISPS} series},
	title = {Partisan {Hearts} and {Minds}: {Political} {Parties} and the {Social} {Identities} of {Voters}},
	isbn = {978-0-300-09215-8},
	shorttitle = {Partisan {Hearts} and {Minds}},
	abstract = {In this, the first major treatment of party identification in twenty years, three political scientists assert that identification with political parties still powerfully determines how citizens look at politics and cast their ballots. Challenging prevailing views, they build a case for the continuing theoretical and political significance of partisan identities.The authors maintain that individuals form partisan attachments early in adulthood and that these political identities, much like religious identities, tend to persist or change only slowly over time. Scandals, recessions, and landslide elections do not greatly affect party identification; large shifts in party attachments occur only when the social imagery of a party changes, as when African Americans became part of the Democratic Party in the South after the passage of the Voting Rights Act. Drawing on a wealth of data analysis using individual-level and aggregate survey data from the United States and abroad, this study offers a new perspective on party identification that will set the terms of discussion for years to come.},
	language = {eng},
	publisher = {Yale University Press},
	author = {Green, Donald and Palmquist, Bradley and Schickler, Eric},
	year = {2002},
	note = {Book Title: Partisan Hearts and Minds},
}

@article{groseclose_measure_2005,
	title = {A {Measure} of {Media} {Bias}},
	volume = {120},
	issn = {0033-5533},
	url = {http://www.jstor.org/stable/25098770},
	abstract = {We measure media bias by estimating ideological scores for several major media outlets. To compute this, we count the times that a particular media outlet cites various think tanks and policy groups, and then compare this with the times that members of Congress cite the same groups. Our results show a strong liberal bias: all of the news outlets we examine, except Fox News' Special Report and the Washington Times, received scores to the left of the average member of Congress. Consistent with claims made by conservative critics, CBS Evening News and the New York Times received scores far to the left of center. The most centrist media outlets were PBS NewsHour, CNN's Newsnight, and ABC's Good Morning America; among print outlets, USA Today was closest to the center. All of our findings refer strictly to news content; that is, we exclude editorials, letters, and the like.},
	number = {4},
	urldate = {2021-12-07},
	journal = {The Quarterly Journal of Economics},
	author = {Groseclose, Tim and Milyo, Jeffrey},
	year = {2005},
	note = {Publisher: Oxford University Press},
	pages = {1191--1237},
}

@article{budak_fair_2016,
	title = {Fair and {Balanced}? {Quantifying} {Media} {Bias} through {Crowdsourced} {Content} {Analysis}},
	volume = {80},
	issn = {0033-362X},
	shorttitle = {Fair and {Balanced}?},
	url = {https://doi.org/10.1093/poq/nfw007},
	doi = {10.1093/poq/nfw007},
	abstract = {It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine-learning and crowdsourcing techniques, we investigate the selection and framing of political issues in fifteen major US news outlets. Starting with 803,146 news stories published over twelve months, we first used supervised learning algorithms to identify the 14 percent of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,502 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. However, news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, major news organizations present topics in a largely nonpartisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, little evidence exists of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet’s ideological position. Finally, news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a convention that further moderates overall differences.},
	number = {S1},
	urldate = {2021-12-07},
	journal = {Public Opinion Quarterly},
	author = {Budak, Ceren and Goel, Sharad and Rao, Justin M.},
	month = jan,
	year = {2016},
	pages = {250--271},
	file = {Snapshot:/Users/bentermaat/Zotero/storage/IBCGMFLD/2223443.html:text/html},
}

@article{gentzkow_what_2010,
	title = {What {Drives} {Media} {Slant}? {Evidence} {From} {U}.{S}. {Daily} {Newspapers}},
	volume = {78},
	issn = {1468-0262},
	shorttitle = {What {Drives} {Media} {Slant}?},
	url = {http://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA7195},
	doi = {10.3982/ECTA7195},
	abstract = {We construct a new index of media slant that measures the similarity of a news outlet's language to that of a congressional Republican or Democrat. We estimate a model of newspaper demand that incorporates slant explicitly, estimate the slant that would be chosen if newspapers independently maximized their own profits, and compare these profit-maximizing points with firms' actual choices. We find that readers have an economically significant preference for like-minded news. Firms respond strongly to consumer preferences, which account for roughly 20 percent of the variation in measured slant in our sample. By contrast, the identity of a newspaper's owner explains far less of the variation in slant.},
	language = {en},
	number = {1},
	urldate = {2021-12-07},
	journal = {Econometrica},
	author = {Gentzkow, Matthew and Shapiro, Jesse M.},
	year = {2010},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA7195},
	pages = {35--71},
	file = {Full Text PDF:/Users/bentermaat/Zotero/storage/47AZBD9I/Gentzkow and Shapiro - 2010 - What Drives Media Slant Evidence From U.S. Daily .pdf:application/pdf;Snapshot:/Users/bentermaat/Zotero/storage/4HZYWEKE/ECTA7195.html:text/html},
}

@article{guess_almost_2021,
	title = {({Almost}) {Everything} in {Moderation}: {New} {Evidence} on {Americans}' {Online} {Media} {Diets}},
	volume = {65},
	issn = {1540-5907},
	shorttitle = {({Almost}) {Everything} in {Moderation}},
	url = {http://onlinelibrary.wiley.com/doi/abs/10.1111/ajps.12589},
	doi = {10.1111/ajps.12589},
	abstract = {Does the internet facilitate selective exposure to politically congenial content? To answer this question, I introduce and validate large-N behavioral data on Americans' online media consumption in both 2015 and 2016. I then construct a simple measure of media diet slant and use machine classification to identify individual articles related to news about politics. I find that most people across the political spectrum have relatively moderate media diets, about a quarter of which consist of mainstream news websites and portals. Quantifying the similarity of Democrats' and Republicans' media diets, I find nearly 65\% overlap in the two groups' distributions in 2015 and roughly 50\% in 2016. An exception to this picture is a small group of partisans who drive a disproportionate amount of traffic to ideologically slanted websites. If online “echo chambers” exist, they are a reality for relatively few people who may nonetheless exert disproportionate influence and visibility.},
	language = {en},
	number = {4},
	urldate = {2021-12-07},
	journal = {American Journal of Political Science},
	author = {Guess, Andrew M.},
	year = {2021},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajps.12589},
	pages = {1007--1022},
	file = {Full Text PDF:/Users/bentermaat/Zotero/storage/5FRT8Q4F/Guess - 2021 - (Almost) Everything in Moderation New Evidence on.pdf:application/pdf;Snapshot:/Users/bentermaat/Zotero/storage/X3QTH6H3/ajps.html:text/html},
}

@article{tyler_partisan_2021,
	title = {Partisan {Enclaves} and {Information} {Bazaars}: {Mapping} {Selective} {Exposure} to {Online} {News}},
	issn = {0022-3816},
	shorttitle = {Partisan {Enclaves} and {Information} {Bazaars}},
	url = {https://www-journals-uchicago-edu.ezp-prod1.hul.harvard.edu/doi/abs/10.1086/716950},
	doi = {10.1086/716950},
	urldate = {2021-12-07},
	journal = {The Journal of Politics},
	author = {Tyler, Matthew and Grimmer, Justin and Iyengar, Shanto},
	month = aug,
	year = {2021},
	note = {Publisher: The University of Chicago Press},
	file = {Full Text PDF:/Users/bentermaat/Zotero/storage/QQETAKQW/Tyler et al. - 2021 - Partisan Enclaves and Information Bazaars Mapping.pdf:application/pdf},
}

@article{converse_nature_2006,
	title = {The nature of belief systems in mass publics (1964)},
	volume = {18},
	issn = {0891-3811},
	url = {https://doi.org/10.1080/08913810608443650},
	doi = {10.1080/08913810608443650},
	number = {1-3},
	urldate = {2021-12-07},
	journal = {Critical Review},
	author = {Converse, Philip E.},
	month = jan,
	year = {2006},
	note = {Publisher: Routledge
\_eprint: https://doi.org/10.1080/08913810608443650},
	pages = {1--74},
	file = {Full Text PDF:/Users/bentermaat/Zotero/storage/MJN7DPLI/Converse - 2006 - The nature of belief systems in mass publics (1964.pdf:application/pdf;Snapshot:/Users/bentermaat/Zotero/storage/L9GHWPAW/08913810608443650.html:text/html},
}

@article{lewis_problem_2021,
	title = {The problem of {Donald} {Trump} and the {Static} {Spectrum} {Fallacy}},
	volume = {27},
	issn = {1354-0688},
	url = {https://doi.org/10.1177/1354068819871673},
	doi = {10.1177/1354068819871673},
	abstract = {Donald Trump’s transformation of Republican Party ideology has helped reveal major problems in the political science discipline’s conceptualization and measurement of ideology. Most scholarship is dominated by the mistaken view that party ideology changes can best be described by parties moving “left” or “right” on a static, ideological, spatial spectrum. In reality, the meaning and content of “left” and “right” (“liberal” and “conservative”) constantly evolve along with the issue positions of the two major parties. Thus, it makes no sense to describe parties as moving to the “left” or “right” over time when the very meanings of “liberalism” and “conservatism” change during the same time period. By understanding the dynamic character of ideology, we can reconcile the paradox of how Trump’s Republican Party can change its ideology even while continuing to be identified with “conservatism” and the “Right.”},
	language = {en},
	number = {4},
	urldate = {2021-12-08},
	journal = {Party Politics},
	author = {Lewis, Verlan},
	month = jul,
	year = {2021},
	note = {Publisher: SAGE Publications Ltd},
	pages = {605--618},
	file = {SAGE PDF Full Text:/Users/bentermaat/Zotero/storage/Y483IUK3/Lewis - 2021 - The problem of Donald Trump and the Static Spectru.pdf:application/pdf},
}

@book{sunstein_republic_2017,
	address = {Princeton, NJ},
	title = {\#{Republic}},
	publisher = {Princeton University Press},
	author = {Sunstein, Cass},
	year = {2017},
}

@book{pariser_filter_2011,
	address = {UK},
	title = {The {Filter} {Bubble}: {What} the {Internet} {Is} {Hiding} from {You}},
	publisher = {Penguin},
	author = {Pariser, Eli},
	year = {2011},
}

@article{yotam_shmargad__samara_klar_sorting_2020,
	title = {Sorting the {News}: {How} {Ranking} by {Popularity} {Polarizes} {Our} {Politics}},
	volume = {37},
	doi = {10.1080/10584609.2020.1713267},
	number = {3},
	journal = {Political Communication},
	author = {{Yotam Shmargad \& Samara Klar}},
	year = {2020},
	pages = {423--446},
}

@article{boxell_greater_2017,
	title = {Greater {Internet} use is not associated with faster growth in political polarization among {US} demographic groups},
	volume = {114},
	issn = {0027-8424},
	url = {http://www.jstor.org/stable/26488105},
	abstract = {We combine eight previously proposed measures to construct an index of political polarization among US adults. We find that polarization has increased the most among the demographic groups least likely to use the Internet and social media. Our overall index and all but one of the individual measures show greater increases for those older than 65 than for those aged 18–39. A linear model estimated at the age-group level implies that the Internet explains a small share of the recent growth in polarization.},
	number = {40},
	urldate = {2021-12-11},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	author = {Boxell, Levi and Gentzkow, Matthew and Shapiro, Jesse M.},
	year = {2017},
	note = {Publisher: National Academy of Sciences},
	pages = {10612--10617},
}

@article{inman_overlapping_1989,
	title = {The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities},
	volume = {18},
	issn = {0361-0926},
	url = {https://doi.org/10.1080/03610928908830127},
	doi = {10.1080/03610928908830127},
	abstract = {The overlapping coefficient is defined as a measure of the agreement between two probability distributions. Its relationship to the dissimilarity index and its propertie are described. An extensive treatment of maximum-likelihood estimation of the overlap between two normal distributions is presented as an example of estimating the overlapping coefficient from sample data.},
	number = {10},
	urldate = {2021-12-11},
	journal = {Communications in Statistics - Theory and Methods},
	author = {Inman, Henry F. and Bradley, Edwin L.},
	month = jan,
	year = {1989},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/03610928908830127},
	pages = {3851--3874},
	file = {Snapshot:C\:\\Users\\Matthew Dardet\\Zotero\\storage\\MDRFRQF5\\03610928908830127.html:text/html},
}

@book{green_partisan_2002,
	address = {New Haven},
	title = {Partisan {Hearts} and {Minds}: {Political} {Parties} and the {Social} {Identities} of {Voters}},
	publisher = {Yale University Press},
	author = {Green, Donald and Palmquist, Bradley and Schickler, Eric},
	year = {2002},
}

@article{clemons_nonparametric_2000,
	title = {A nonparametric measure of the overlapping coefficient},
	volume = {34},
	issn = {0167-9473},
	url = {https://www.sciencedirect.com/science/article/pii/S0167947399000742},
	doi = {10.1016/S0167-9473(99)00074-2},
	abstract = {We examine the sampling behavior of a nonparametric estimator of the overlapping coefficient. The overlapping coefficient is a proposed measure of the discrepancy between two independent samples. Using Monte Carlo techniques, it is discovered that the sampling estimator of the overlapping coefficient using the naive kernel density estimator is biased. The bias of the kernel estimator of the overlapping coefficient increases as the similarity of the distributions from which the samples are obtained increases. Yet, the bias of the estimator in most cases is minimal. A bootstrap estimator of the sampling standard deviation of the nonparametric estimator of the overlapping coefficient was also examined. The behavior of the sampling estimator of the overlap suggest that the overlapping coefficient can best serve as a valuable check in investigation of differences detected between two distributions by standard statistical techniques.},
	language = {en},
	number = {1},
	urldate = {2021-12-11},
	journal = {Computational Statistics \& Data Analysis},
	author = {Clemons, Traci E and Bradley, Edwin L},
	month = jul,
	year = {2000},
	pages = {51--61},
	file = {ScienceDirect Snapshot:/Users/bentermaat/Zotero/storage/2XSBZCRN/S0167947399000742.html:text/html},
}

@article{king_how_2015,
	title = {How {Robust} {Standard} {Errors} {Expose} {Methodological} {Problems} {They} {Do} {Not} {Fix}, and {What} to {Do} {About} {It}},
	volume = {23},
	issn = {1047-1987},
	url = {http://www.jstor.org/stable/24572966},
	abstract = {"Robust standard errors" are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, settling for a misspecified model, with or without robust standard errors, will still bias estimators of all but a few quantities of interest. The resulting cavernous gap between theory and practice suggests that considerable gains in applied statistics may be possible. We seek to help researchers realize these gains via a more productive way to understand and use robust standard errors; a new general and easier-to-use "generalized information matrix test" statistic that can formally assess misspecification (based on differences between robust and classical variance estimates); and practical illustrations via simulations and real examples from published research. How robust standard errors are used needs to change, but instead of jettisoning this popular tool we show how to use it to provide effective clues about model misspecification, likely biases, and a guide to considerably more reliable, and defensible, inferences. Accompanying this article is software that implements the methods we describe.},
	number = {2},
	urldate = {2021-12-11},
	journal = {Political Analysis},
	author = {King, Gary and Roberts, Margaret E.},
	year = {2015},
	note = {Publisher: [Oxford University Press, Society for Political Methodology]},
	pages = {159--179},
}

@article{aronow_note_2016,
	title = {A {Note} on "{How} {Robust} {Standard} {Errors} {Expose} {Methodological} {Problems} {They} {Do} {Not} {Fix}, and {What} to {Do} {About} {It}"},
	url = {https://arxiv.org/abs/1609.01774v1},
	abstract = {King and Roberts (2015, KR) claim that a disagreement between robust and classical standard errors exposes model misspecification. We emphasize that KR's claim only generally applies to parametric models: models that assume a restrictive form of the distribution of the outcome. Many common models in use in political science, including the linear model, are not necessarily parametric -- rather they may be semiparametric. Common estimators of model parameters such as ordinary least squares have both robust (corresponding to a semiparametric model) and classical (corresponding to a more restrictive model) standard error estimates. Given a properly specified semiparametric model and mild regularity conditions, the classical standard errors are not generally consistent, but the robust standard errors are. To illustrate this point, we consider the case of the regression estimate of a semiparametric linear model with no model misspecification, and show that robust standard errors may nevertheless systematically differ from classical standard errors. We show that a disagreement between robust and classical standard errors is not generally suitable as a diagnostic for regression estimators, and that KR's reanalyses of Neumayer (2003) and B{\textbackslash}"uthe and Milner (2008) are predicated on strong assumptions that the original authors did not invoke nor require.},
	language = {en},
	urldate = {2021-12-11},
	author = {Aronow, Peter M.},
	month = sep,
	year = {2016},
	file = {Full Text PDF:/Users/bentermaat/Zotero/storage/ZPAGCATH/Aronow - 2016 - A Note on How Robust Standard Errors Expose Metho.pdf:application/pdf;Snapshot:/Users/bentermaat/Zotero/storage/69AP44IN/1609.html:text/html},
}

@article{lei_distribution-free_2018,
	title = {Distribution-{Free} {Predictive} {Inference} for {Regression}},
	volume = {113},
	issn = {0162-1459},
	url = {https://doi.org/10.1080/01621459.2017.1307116},
	doi = {10.1080/01621459.2017.1307116},
	abstract = {We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a prediction band for the response variable using any estimator of the regression function. The resulting prediction band preserves the consistency properties of the original estimator under standard assumptions, while guaranteeing finite-sample marginal coverage even when these assumptions do not hold. We analyze and compare, both empirically and theoretically, the two major variants of our conformal framework: full conformal inference and split conformal inference, along with a related jackknife method. These methods offer different tradeoffs between statistical accuracy (length of resulting prediction intervals) and computational efficiency. As extensions, we develop a method for constructing valid in-sample prediction intervals called rank-one-out conformal inference, which has essentially the same computational efficiency as split conformal inference. We also describe an extension of our procedures for producing prediction bands with locally varying length, to adapt to heteroscedasticity in the data. Finally, we propose a model-free notion of variable importance, called leave-one-covariate-out or LOCO inference. Accompanying this article is an R package conformalInference that implements all of the proposals we have introduced. In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.},
	number = {523},
	urldate = {2021-12-11},
	journal = {Journal of the American Statistical Association},
	author = {Lei, Jing and G’Sell, Max and Rinaldo, Alessandro and Tibshirani, Ryan J. and Wasserman, Larry},
	month = jul,
	year = {2018},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/01621459.2017.1307116},
	pages = {1094--1111},
	file = {Full Text PDF:/Users/bentermaat/Zotero/storage/WW9FH89E/Lei et al. - 2018 - Distribution-Free Predictive Inference for Regress.pdf:application/pdf;Snapshot:/Users/bentermaat/Zotero/storage/9GN6M6DR/01621459.2017.html:text/html},
}
